The Application of Deep Learning in Tactical Analysisof Football Matches

The Application of Deep Learning in Tactical Analysisof Football Matches | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Application of Deep Learning in Tactical Analysisof Football Matches Wuyu Huang, Sihang Wang, Pei Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7258508/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract The integration of deep learning techniques into football match analysis has revolutionized the understanding of tactical behaviors. Advanced models, such as Tactiformer and StratGaze, have been developed to address the complexities of multi-agent interactions and dynamic strategies inherent in football. Tactiformer employs a hierarchical transformer architecture to capture spatiotemporal patterns, enabling the encoding of player movements, team formations, and strategic intents across different levels of abstraction. Building upon this, StratGaze introduces a strategic attention mechanism that facilitates the inference of tactical motifs and supports predictive modeling of game trajectories. These frameworks collectively enhance the capability to analyze, interpret, and predict tactical evolutions in football matches, offering valuable insights for coaches, analysts, and stakeholders in the sport. Moreover, by leveraging vast datasets of positional and event-based information, these models can identify subtle patterns and contextual cues that were previously difficult to discern through manual observation or traditional statistical methods. The resulting analytical granularity not only aids in retrospective performance evaluation but also provides a robust foundation for real-time decision support systems. This marks a significant leap forward in football intelligence, bridging the gap between raw data and actionable strategic insights. The continuous evolution of such models, integrating domain-specific priors and multi-modal data, promises even deeper tactical comprehension, ultimately reshaping both professional match preparation and broader football analytics research. Physical sciences/Engineering Physical sciences/Mathematics and computing Football Tactics Spatiotemporal Analysis Transformer Architecture Strategic Attention Multi-Agent Systems Predictive Analytics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Sep, 2025 Reviews received at journal 18 Aug, 2025 Reviews received at journal 15 Aug, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviews received at journal 09 Aug, 2025 Reviewers agreed at journal 09 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers invited by journal 07 Aug, 2025 Editor invited by journal 05 Aug, 2025 Editor assigned by journal 04 Aug, 2025 Submission checks completed at journal 01 Aug, 2025 First submitted to journal 31 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7258508","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":499046641,"identity":"2bdeda6c-a293-41d2-8bd4-20423a90b96d","order_by":0,"name":"Wuyu Huang","email":"","orcid":"","institution":"Guangzhou Institute of Navigation","correspondingAuthor":false,"prefix":"","firstName":"Wuyu","middleName":"","lastName":"Huang","suffix":""},{"id":499046642,"identity":"358c1c6d-61d6-4d7a-ba34-0970140f623f","order_by":1,"name":"Sihang Wang","email":"","orcid":"","institution":"South China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Sihang","middleName":"","lastName":"Wang","suffix":""},{"id":499046645,"identity":"4463096f-914f-4890-a405-19860402891b","order_by":2,"name":"Pei Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACAzjJDMQfG0A8xsYDRGthnNnAIAGkGojQAgXMvGAtDAx4tZizn3346EbBncTt7cwPP9vusKnTbT8MtKXGJhqXFsuedGPjHINniXMOsxlL555JkzA7kwjUciwttwGXww6ksUnnGBxOnMHMYCCd23ZYwuwAUAtjw2HcWs4/Y/8N0cL++bclSMv5hwS03EhjY4Zo4TGTZgRpuUHIlhvPmEEOMwZqKbPsbUuT3HYDaEsCPr+cT2P8nPPnsOwM/uObb/xss+E3O5/+8MGHGhucWnCABNKUj4JRMApGwShAAwBpgWCBmVjAZgAAAABJRU5ErkJggg==","orcid":"","institution":"South China Normal University","correspondingAuthor":true,"prefix":"","firstName":"Pei","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-07-31 06:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7258508/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7258508/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89063808,"identity":"43019dd5-4a95-4d14-8a33-228baccf11e0","added_by":"auto","created_at":"2025-08-14 10:07:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2291518,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7258508/v1_covered_89c57def-c387-4f66-9da5-92f8ec94c784.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Application of Deep Learning in Tactical Analysisof Football Matches","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Football Tactics, Spatiotemporal Analysis, Transformer Architecture, Strategic Attention, Multi-Agent Systems, Predictive Analytics","lastPublishedDoi":"10.21203/rs.3.rs-7258508/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7258508/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe integration of deep learning techniques into football match analysis has revolutionized the understanding of tactical behaviors. 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